Challenges in Batch Optimization: Recently, there has been a strong interest in solving several instances of convex quadratic programming (QP) in parallel [1]}, [2]}. The core innovation in [1]} lies in rewriting the underlying matrix-algebra such that matrices that do not change with the batch index can be isolated, and their factorization can be pre-stored. Our work achieves a similar structure but for a much more challenging constrained non-convex problem. Note: Gradient-descent can be trivially batched but it performed poorly on (REF )-() in our experiments.
